A review of clustering techniques and developments

Saxena, A; Prasad, M; Gupta, A; Bharill, N; Patel, OP; Tiwari, A; Er, MJ; Ding, W; Lin, CT

A review of clustering techniques and developments

Saxena, A Prasad, M

Gupta, A Bharill, N Patel, OP Tiwari, A Er, MJ Ding, W Lin, CT

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2017, 267 pp. 664 - 681
Issue Date:: 2017-12-06

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Field	Value	Language
dc.contributor.author	Saxena, A	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Gupta, A	en_US
dc.contributor.author	Bharill, N	en_US
dc.contributor.author	Patel, OP	en_US
dc.contributor.author	Tiwari, A	en_US
dc.contributor.author	Er, MJ	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.available	2020-05-25T19:27:17Z
dc.date.issued	2017-12-06	en_US
dc.identifier.citation	Neurocomputing, 2017, 267 pp. 664 - 681	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114834
dc.description.abstract	© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101645
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2017.06.053	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A review of clustering techniques and developments	en_US
dc.type	Journal Article
utslib.citation.volume	267	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	267	en_US

Abstract:

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.

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http://hdl.handle.net/10453/114834